Automatic bilingual lexicon acquisition using random indexing of parallel corpora

Sahlgren, Magnus; Karlgren, Jussi

doi:10.1017/s1351324905003876

Cited by 46 publications

(49 citation statements)

References 15 publications

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“…These index vectors are sparse, high-dimensional, and ternary, which means that their dimensionality (d) is on the order of hundreds, and that they consist of a small number( ) of randomly distributed +1s and -1s, with the rest of the elements of the vectors set to 0. In our work each element is allocated with the following probability [13]:…”

Section: Random Indexing (Ri)mentioning

confidence: 99%

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A Random Indexing Approach for Web User Clustering and Web Prefetching

Wan

Jönsson

et al. 2012

New Frontiers in Applied Data Mining

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Abstract. In this paper we present a novel technique to capture Web users' behaviour based on their interest-oriented actions. In our approach we utilise the vector space model Random Indexing to identify the latent factors or hidden relationships among Web users' navigational behaviour. Random Indexing is an incremental vector space technique that allows for continuous Web usage mining. User requests are modelled by Random Indexing for individual users' navigational pattern clustering and common user profile creation. Clustering Web users' access patterns may capture common user interests and, in turn, build user profiles for advanced Web applications, such as Web caching and prefetching. We present results from the Web user clustering approach through experiments on a real Web log file with promising results. We also apply our data to a prefetching task and compare that with previous approaches. The results show that Random Indexing provides more accurate prefetchings.

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Section: Random Indexing (Ri)mentioning

confidence: 99%

“…Since 2000, it has been studied and empirically validated in a number of experiments and usages in distributional similarity problems [12,13]. However, few of the Random Indexing approaches have been employed into the field of Web mining, especially for the discovery of Web user access patterns.…”

Section: Introductionmentioning

confidence: 99%

A Random Indexing Approach for Web User Clustering and Web Prefetching

Wan

Jönsson

et al. 2012

New Frontiers in Applied Data Mining

View full text Add to dashboard Cite

show abstract

“…Such considerations taken into account a word space model can be applied to basically any language. For instance, word space models have been applied to, among many other languages; English, German, Spanish, Swedish as well as Japanese (Hassel 2005, Sahlgren & Karlgren 2005, Sahlgren 2006). …”

Section: Word Space Modelsmentioning

confidence: 99%

Applied Natural Language Processing

Shwartz¹

2012

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Today, with digitally stored information available in abundance, even for many less commonly spoken languages, this information must by some means be filtered and extracted in order to avoid drowning in it. Automatic summarization is one such technique, where a computer summarizes a longer text into a shorter non-redundant form. The development of advanced summarization systems also for smaller languages may unfortunately prove too costly. Nevertheless, there will still be a need for summarization tools for these languages in order to curb the immense flow of digital information. This chapter sets the focus on automatic summarization of text using as few direct human resources as possible, resulting in what can be perceived as an intermediary system. Furthermore, it presents the notion of taking a holistic view of the generation of summaries.

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“…Sahlgren & Karlgren [21] demonstrated that Random Indexing can be applied to parallel texts for automatic bilingual lexicon acquisition. Sahlgren & Cöster [7] used Random Indexing to carry out text categorization.…”

Section: Random Indexingmentioning

confidence: 99%

Concept Based Representations as Complement of Bag of Words in Information Retrieval

Carrillo

2010

IFIP Advances in Information and Communication Technology

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Abstract. Information Retrieval models, which do not represent texts merely as collections of the words they contain, but rather as collections of the concepts they contain through synonym sets or latent dimensions, are known as Bag-of-Concepts (BoC) representations. In this paper we use random indexing, which uses co-occurrence information among words to generate semantic context vectors and then represent the documents and queries as BoC. In addition, we use a novel representation, Holographic Reduced Representation, previously proposed in cognitive models, which can encode relations between words. We show that these representations can be successfully used in information retrieval, can associate terms, and when they are combined with the traditional vector space model, they improve effectiveness, in terms of mean average precision.

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Automatic bilingual lexicon acquisition using random indexing of parallel corpora

Cited by 46 publications

References 15 publications

A Random Indexing Approach for Web User Clustering and Web Prefetching

A Random Indexing Approach for Web User Clustering and Web Prefetching

Applied Natural Language Processing

Concept Based Representations as Complement of Bag of Words in Information Retrieval

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